Sep 7, 2021

How To Improve Your Email Open Rates Significantly With Personalization

Understand your customers better than they understand themselves, and target messaging to them based on their persona to significantly improve your email open rates.

Shanif Dhanani

The hardest part about scaling an ecommerce store is figuring out the marketing engine that is cost-efficient and can hit the right customers at the right time with the right messaging. Sounds impossible right? Well, actually there’s a very simple thing that you can do to start building a clearer picture of who you are speaking to in your messages.

You’ve heard of all these different email strategies and paid ads that you can try to acquire more customers, but unless you have an idea who you’re speaking to, it’s all pointless. Before you get into the personalization and segmentation process, it’s very important to build out your customer personas first.

If we use an example, say Lululemon’s customer base, we can start to see how and why segmenting your customers might help. Some of their personas may include: (1) the active millennial male who lives in the city and commutes to work, as well as (2) the working mom who’s juggling everything at home while trying to stay fit. The way that you market to each of these groups is totally different. You don’t want to be sending an offer on blue tights to the active millennial male, and you don’t want to be sending the 9” liner short to the working mom. These are extreme cases, but you can see why this might make a difference.

In order to make your messages and marketing compelling for your customers, you need to understand how they think, what voice and tone they prefer to hear, and what motivates them personally.

This is why we suggest marketers perform a deep user persona analysis of their customers. Here are the main categories do break down your user into a data-driven approach, so that you can tailor your messaging to them better.

Here are some questions you can ask yourself about each user group:

Part 1: Demographics
  • Where do these people live?
  • Are they single, married, do they have children?
  • What level of education do they have?
  • What is their typical individual or household income?
  • How old are they?
  • What gender or race are they?
Part 2: Psychographics
  • How do they spend their free time? Do they even have free time?
  • What are they personally interested in?
  • What type of lifestyle do they have? Are they spending most time inside or outside?
Part 3: Behaviors
  • How do customers currently buy your product? Do they go directly to your website, are they finding you through influencers, or do they buy on Instagram?
  • What is their experience when they first get the product?
  • After they receive the product, how do they use it? How often do they use it?
  • At what point do they fall in love with the product after buying?
Part 4: Motivations
  • Why do they want to buy and use your product?
  • What are the customer’s biggest pain points and how do you solve it for them?
Part 5: Influences
  • Who influences their decisions on purchases?
  • What influences their decisions to buy something?

After you’re done with this exercise, you should have anywhere between two to five core groups of customers that you can start thinking about how to personalize marketing and advertisements on Facebook, Google, Instagram, and emails.

There aren’t that many easy to use tools out there that can help you segment out your customer groups in practice within your marketing stack, which is why we built Apteo.

If you’d like to put these personalizations into practice with a free and easy to use tool, please sign up for a demo here. We will be launching on the Shopify App Store in February, so if you’d like to be on the list please fill out this form to get notified when it’s out!


Image by Windows on Unsplash

About the author
Shanif Dhanani
Co-Founder and CEO, Apteo

Shanif Dhanani is the co-founder & CEO of Apteo. Prior to Apteo, Shanif was a data scientist and software engineer at Twitter, and prior to that he was the lead engineer and head of analytics at TapCommerce, a NYC-based ad tech startup acquired by Twitter. He has a passion for all things data and analytics, loves adventure traveling, and generally loves living in New York City.